Explore global development with R

Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.

Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.

Get the necessary packages

First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.8     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.1
## ✔ readr   2.1.2     ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

Look at the data and tackle the tasks

First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.

str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
##  $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
##  [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 × 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.

Let’s plot all the countries in 1952.

theme_set(theme_bw())  # set theme to white background for better visibility

ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()

…

We see an interesting spread with an outlier to the right. Answer the following questions, please:

Question 1

Why does it make sense to have a log10 scale on x axis?

It makes sense to use a log10 scale as the growth in population is so rapid, that it would not fit within the graph otherwise.

Question 2

Who is the outlier (the richest country in 1952 - far right on x axis)?

As seen in table below, Kuwait has a significantly higher GDP per capita than the others. The outlier is Kuwait.

gapminder %>%
    filter( year == 1952) %>% 
    group_by(country) %>% 
    summarize(max_gdpPercap = max(gdpPercap)) %>%
    arrange(desc(max_gdpPercap)) %>% 
    head() %>% 
  knitr::kable(caption = "Richest countries in 1952", 
               col.names = c("Country", "GDP per capita"))
Richest countries in 1952
Country GDP per capita
Kuwait 108382.35
Switzerland 14734.23
United States 13990.48
Canada 11367.16
New Zealand 10556.58
Norway 10095.42

Next, you can generate a similar plot for 2007 and compare the differences

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()

…

The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.

Tasks:

Question 3

Differentiate the continents by color, and fix the axis labels and units to be more legible (Hint: the 2.50e+08 is so called “scientific notation”, which you might want to eliminate)

When making the graph more readable, I import the "scales" library to deal with the scientific notation.
library (scales) # attaching library scales
## 
## Vedhæfter pakke: 'scales'
## Det følgende objekt er maskeret fra 'package:purrr':
## 
##     discard
## Det følgende objekt er maskeret fra 'package:readr':
## 
##     col_factor
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop, col = continent)) +  # differentiate continents by color
  geom_point(alpha = 0.6) + # add transparency to account for overlap
  labs(
    title = "2007",
    size = "Population",
    col = "Continent",
    x = "GDP per capita", 
    y = "Life expectancy") +
  scale_x_log10(labels = comma) + # removing scientific notation
  scale_size(labels = comma) + # removing scientific notation
    theme_gray()

Question 4

What are the five richest countries in the world in 2007?

The five countries with the highest GDP per capita in 2007 are, as seen below, Norway, Kuwait, Singapore, United States, and Ireland.

gapminder %>%
    filter( year == 2007) %>% 
    group_by(country) %>% 
    summarize(max_gdpPercap = max(gdpPercap)) %>%
    arrange(desc(max_gdpPercap)) %>% 
    head(5,) %>% 
  knitr::kable(caption = "Richest countries in 2007", 
               col.names = c("Country", "GDP per capita"))
Richest countries in 2007
Country GDP per capita
Norway 49357.19
Kuwait 47306.99
Singapore 47143.18
United States 42951.65
Ireland 40676.00

Make it move!

The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. Beware that there may be other packages your operating system needs in order to glue interim images into an animation or video. Read the messages when installing the package.

Also, there are two ways of animating the gapminder ggplot.

Option 1: Animate using transition_states()

The first step is to create the object-to-be-animated

anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()  # convert x to log scale
anim

…

This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the bottom right ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the visual inside an html file.

anim + transition_states(year, 
                      transition_length = 1,
                      state_length = 1)

…

Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.

Option 2 Animate using transition_time()

This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.

anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() + # convert x to log scale
  transition_time(year)
anim2

The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.

Now, choose one of the animation options and get it to work. You may need to troubleshoot your installation of gganimate and other packages

Question 5

Can you add a title to one or both of the animations above that will change in sync with the animation?(Hint: search labeling for transition_states() and transition_time() functions respectively)

Added title to transition_states() animation.

anim + 
  transition_states(year, 
                         transition_length = 1, 
                         state_length = 1) +
  labs(title = "{closest_state}") # add changing title

Added title to transition_time() animation.

anim2_title <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() +
  transition_time(year) +
  labs(title = "Year: {frame_time}") # add changing title
anim2_title

Question 6

Can you made the axes’ labels and units more readable? Consider expanding the abbreviated labels as well as the scientific notation in the legend and x axis to whole numbers.

Added labels and cleaned up the visuals of the transition_time() animation.

anim2_readable <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, col = continent)) + # differentiate continents by color
  geom_point(alpha = 0.6) +  # add transparency to account for overlap
  transition_time(year) +
  labs(title = "Year: {frame_time}",
       size = "Population",
       col = "Continent",
       x = "GDP per capita", 
       y = "Life expectancy") +
  scale_x_log10(labels = comma) + # remove scientific notation
  scale_size(labels = comma) + # remove scientific notation
    theme_gray()
anim2_readable

Question 7

Come up with a question you want to answer using the gapminder data and write it down. Then, create a data visualization that answers the question and explain how your visualization answers the question. (Example: you wish to see what was mean life expectancy across the continents in the year you were born versus your parents’ birth years).

I want to explore how has different continents’ life expectancy has changed over time. To do this, I group the data by year and continent and find the median for each year and continent and plot those as points with lines connecting them.

gapminder %>%
  group_by(year, continent) %>% # group by year and continent
  summarise(lifeExp=median(lifeExp)) %>% # find the median
  ggplot(aes(x=year, y=lifeExp, color=continent)) +
    geom_point(size=1.5) + 
    geom_line(size=1) + #joining the points
    labs(
      title = "Life expectancy over time", 
      col = "Continent",
      x = "Year",
      y = "Life expectancy") +
    theme_gray()
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.